DataCompute Agent — AI-Powered Compute-Over-Data on Filecoin

Autonomous MCP server that fetches datasets from IPFS/Filecoin, performs computation (anomaly detection, statistical analysis, data quality scoring), and stores results back via Multi-Chain Storage (MCS) simulation.

Built for Data DAO Hackathon (Filecoin/DoraHacks) — Tracks 2 & 3 ($35K combined).

🎯 Problem

Data stored on Filecoin/IPFS is growing rapidly, but performing computation on that data requires manual retrieval, local processing, and re-upload — a slow, fragmented workflow. There's no autonomous agent layer that can fetch datasets, perform analysis, and store verified results back on-chain.

✅ Solution

DataCompute Agent is an MCP (Model Context Protocol) server with 6 callable tools that provides autonomous compute-over-data on Filecoin/IPFS datasets:

1. Fetch datasets from IPFS by CID via public gateways
2. Compute statistics (mean, median, std dev, quartiles)
3. Detect anomalies using Z-score and IQR methods
4. Score data quality (completeness, uniqueness, consistency)
5. Store results to Filecoin via MCS (Multi-Chain Storage) simulation
6. Pipeline — run the complete fetch → compute → detect → quality → store cycle

🏆 Unique Angle

Unlike existing Filecoin MCP servers that only handle storage (foc-storage-mcp, storacha/mcp), DataCompute Agent adds a computation layer — it doesn't just store/retrieve data, it analyzes data in-transit and stores verified results back on-chain. First MCP server to combine compute-over-data with multi-chain storage on Filecoin.

📐 Architecture

IPFS/Filecoin (Storage)              DataCompute Agent (MCP)
+-----------------------+           +--------------------------+
|  Dataset (CID)         |          |  1. fetch_dataset()      |
|  CSV, JSON, JSONL     |--------->|  2. compute_statistics()  |
|                        |          |  3. detect_anomalies()   |
|  Results (via MCS)    |<---------|  4. data_quality_score()  |
|  CID pinning          |          |  5. store_results()       |
+-----------------------+          |  6. full_pipeline()       |
                                    +--------------------------+
                                             |
                                             v
                                    +--------------------------+
                                    |  FastAPI Web Dashboard    |
                                    |  - Submit CID for analysis |
                                    |  - View results & reports  |
                                    |  - MCP tool explorer       |
                                    +--------------------------+

🛠️ MCP Tools (6 callable)

#	Tool	Description	Endpoint
1	fetch_dataset	Retrieve dataset from IPFS by CID	GET /api/tools/fetch?cid=<CID>
2	compute_statistics	Descriptive statistics on columns	GET /api/tools/statistics?cid=<CID>
3	detect_anomalies	Z-score / IQR anomaly detection	GET /api/tools/anomalies?cid=<CID>&method=zscore
4	data_quality_score	Completeness, uniqueness, consistency	GET /api/tools/quality?cid=<CID>
5	store_results	Store results via MCS simulation	POST /api/tools/store
6	full_pipeline	Complete fetch→compute→detect→store	POST /api/tools/pipeline

🚀 Setup

git clone https://github.com/0xConsole/datacompute-agent.git
cd datacompute-agent
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

Open http://localhost:8000 for the dashboard.

🌐 Live Demo

URL: https://datacompute-agent.vercel.app

📊 Tech Stack

Component	Technology
Backend	Python + FastAPI
MCP Interface	MCP Server pattern (6 callable tools)
IPFS Gateway	ipfs.io / dweb.link / cloudflare-ipfs
MCS Simulation	FilSwan MCS API simulation
Data Processing	pandas, numpy
Anomaly Detection	Z-score, IQR methods
Storage	SQLite (audit trail)
Deployment	Vercel

🔍 What's Real vs Mocked

Component	Status
IPFS dataset retrieval	✅ Real (public IPFS gateways)
Data computation (stats, anomalies)	✅ Real (pandas/numpy)
MCP tool interface	✅ Real (FastAPI endpoints)
Data quality scoring	✅ Real
MCS storage simulation	⚠️ Mocked (simulates FilSwan MCS API)
Filecoin deal-making	⚠️ Mocked (no FIL tokens needed)

All mockable components are behind interfaces — swap in real MCS SDK and Filecoin deals when FIL is available.

🏷️ Tracks Covered

• Track 2 — Multi-Chain Storage ($20K): Results stored via MCS simulation, cross-chain storage gateway integration
• Track 3 — Computing Over Data ($15K): Core functionality — computation on data retrieved from Filecoin

🔗 Links

• Live Demo: https://datacompute-agent.vercel.app
• GitHub: https://github.com/0xConsole/datacompute-agent
• Filecoin Docs: https://docs.filecoin.io
• FilSwan MCS: https://docs.filswan.com/multi-chain-storage
• IPFS: https://docs.ipfs.io

📄 License

MIT